System and method for a cognitive system plug-in answering subject matter expert questions

ABSTRACT

Embodiments provide a system and method for integrating a cognitive system into a call center. The system and method include ingesting, in real-time through an instant messaging application, one or more questions from one or more call center agents; ingesting, in real-time through the instant messaging application, one or more answers associated with the one or more questions; storing one or more question and answer pairs in a corpus; analyzing, through a cognitive system, the corpus of the one or more question and answer pairs; receiving, through the instant messaging system, one or more additional questions; determining a proposed answer to each additional question based on the analysis of the corpus; analyzing, through the cognitive system, the proposed answer; and incorporating the analysis of the proposed answer into the analysis of the one or more question and answer pairs. The answers can be provided by subject matter experts or call center managers. The cognitive system can interface with the instant messaging system through the use of a plug-in module, which can use one or more registration commands to facilitate the interface.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/208,134, filed on Jul. 12, 2016, the entire contents of which ishereby incorporated by reference herein.

TECHNICAL FIELD

The present application relates generally to a system and method thatcan be used to practically integrate cognitive technology into thesystems of a call center.

BACKGROUND

Call centers have evolved over the past decades into highly efficientsystems. Introducing a discontinuous technology solution into anefficient call center can adversely impact key performance metrics suchas average handle time and customer satisfaction. This can pose achallenge for introducing cognitive technology into a call center, asmany traditional products require a training period to adapt the systemto the specific use case.

Call center agents rely on collaboration and guidance from seasoned teammembers (also known as subject matter experts (SME)) to effectively andefficiently serve customers. Prior art cognitive products require amanual training period to adapt the solution to the specific industrydomain and to train models against the natural language utterancescommon to the specific use case. During this training period, theability of the prior art cognitive systems to return acceptable answersis relatively low. Traditional models require the call center agents tocorrect the system when an incorrect answer is given, but typically callcenter agents have little time to perform this function and will discardsystems that are overly cumbersome.

SUMMARY

Embodiments can provide a computer implemented method, in a dataprocessing system comprising a processor and a memory comprisinginstructions which are executed by the processor to cause the processorto implement a call center question and answer system, the methodcomprising ingesting, in real-time through an instant messagingapplication, one or more questions from one or more call center agents;ingesting, in real-time through the instant messaging application, oneor more answers associated with the one or more questions; storing oneor more question and answer pairs in a corpus; analyzing, through acognitive system, the corpus of the one or more question and answerpairs; receiving, through the instant messaging system, one or moreadditional questions; determining a proposed answer to each additionalquestion based on the analysis of the corpus; analyze, through thecognitive system, the proposed answer; and incorporate the analysis ofthe proposed answer into the analysis of the one or more question andanswer pairs.

Embodiments can further provide a method further comprising utilizing aplug-in module to moderate the interactions between the cognitive systemand the instant messaging application without providing an externalnotification.

Embodiments can further provide a method wherein the plug-in moduleutilizes one or more registration commands to identify the one or morequestions and answers sent between the instant messaging application andthe cognitive system.

Embodiments can further provide a method further comprising ingesting,through the instant messaging application, one or more answers providedby one or more subject matter experts.

Embodiments can further provide a method further comprising providingthe proposed answer to the call center agent through the instantmessaging application.

Embodiments can further provide a method further comprising receiving,through a feedback module, feedback on the proposed answer from one ormore subject matter experts or call center managers.

Embodiments can further provide a method further comprisingincorporating the feedback into the analysis of the corpus.

In another illustrative embodiment, a computer program productcomprising a computer usable or readable medium having a computerreadable program is provided. The computer readable program, whenexecuted on a processor, causes the processor to perform various onesof, and combinations of, the operations outlined above with regard tothe method illustrative embodiment.

Embodiments can further provide a method for cognitive systemintegration into a call center using an instant messaging application,the method comprising ingesting one or more questions entered into theinstant messaging application by one or more call center agents;ingesting one or more answers associated with the one or more questionsentered into the instant messaging application; storing one or morequestion and answer pairs in a corpus; analyzing the corpus of the oneor more question and answer pairs with a cognitive system; receiving oneor more additional questions entered into the instant messagingapplication; determining a proposed answer to each additional questionbased on the cognitive system's analysis of the corpus; and providingthe proposed answer to the call center agent.

Embodiments can further provide a method further comprising ingestingone or more answers provided by one or more subject matter expertsthrough the instant messaging application.

Embodiments can further provide a method further comprising receivingfeedback on the proposed answer from one or more subject matter expertsor call center managers through a feedback module; and incorporating thefeedback into the analysis of the corpus.

Additional features and advantages of this disclosure will be madeapparent from the following detailed description of illustrativeembodiments that proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other aspects of the present invention are bestunderstood from the following detailed description when read inconnection with the accompanying drawings. For the purpose ofillustrating the invention, there is shown in the drawings embodimentsthat are presently preferred, it being understood, however, that theinvention is not limited to the specific instrumentalities disclosed.Included in the drawings are the following Figures:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of acognitive system implementing a call center question and answer (QA)system in a computer network;

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented;

FIG. 3 illustrates a QA system pipeline, of a cognitive system, forprocessing an input question generated from the call center QA system inaccordance with one illustrative embodiment; and

FIG. 4 illustrates a flowchart showing the integration process by whicha cognitive system can be introduced into a working call center withoutdisrupting the call center's functionality, according to illustrativeembodiments as described herein.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The present description and claims may make use of the terms “a,” “atleast one of,” and “one or more of,” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within in thescope of the description and claims.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples are intendedto be non-limiting and are not exhaustive of the various possibilitiesfor implementing the mechanisms of the illustrative embodiments. It willbe apparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the example provided herein without departing from thespirit and scope of the present invention.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a head disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network(LAN), a wide area network (WAN) and/or a wireless network. The networkmay comprise copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computers,and/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including anobject-oriented programming language such as Java, Smalltalk, C++ or thelike, and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computer,or entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including LAN or WAN, or the connection may be made toan external computer (for example, through the Internet using anInternet Service Provider). In some embodiments, electronic circuitryincluding, for example, programmable logic circuitry, field-programmablegate arrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operations steps to be performed on the computer,other programmable apparatus, or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical functions. In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As an overview, a cognitive system is a specialized computer system, orset of computer systems, configured with hardware and/or software logic(in combination with hardware logic upon which the software executes) toemulate human cognitive functions. These cognitive systems applyhuman-like characteristics to conveying and manipulating ideas which,when combined with the inherent strengths of digital computing, cansolve problems with high accuracy and resilience on a large scale. IBMWatson™ is an example of one such cognitive system which can processhuman readable language and identify inferences between text passageswith human-like accuracy at speeds far faster than human beings and on amuch larger scale. In general, such cognitive systems are able toperform the following functions:

-   -   Navigate the complexities of human language and understanding    -   Ingest and process vast amounts of structured and unstructured        data    -   Generate and evaluate hypotheses    -   Weigh and evaluate responses that are based only on relevant        evidence    -   Provide situation-specific advice, insights, and guidance    -   Improve knowledge and learn with each iteration and interaction        through machine learning processes    -   Enable decision making at the point of impact (contextual        guidance)    -   Scale in proportion to the task    -   Extend and magnify human expertise and cognition    -   Identify resonating, human-like attributes and traits from        natural language    -   Deduce various language specific or agnostic attributes from        natural language    -   High degree of relevant recollection from data points (images,        text, voice) (memorization and recall)    -   Predict and sense with situation awareness that mimic human        cognition based on experiences    -   Answer questions based on natural language and specific evidence

In one aspect, cognitive systems provide mechanisms for answeringquestions posed to these cognitive systems using a Question Answeringpipeline or system (QA system). The QA pipeline or system is anartificial intelligence application executing on data processinghardware that answers questions pertaining to a given subject-matterdomain presented in natural language. The QA pipeline receives inputsfrom various sources including input over a network, a corpus ofelectronic documents or other data, data from a content creator,information from one or more content users, and other such inputs fromother possible sources of input. Data storage devices store the corpusof data. A content creator creates content in a document for use as partof a corpus of data with the QA pipeline. The document may include anyfile, text, article, or source of data for use in the QA system. Forexample, a QA pipeline accesses a body of knowledge about the domain, orsubject matter area (e.g., financial domain, medical domain, legaldomain, etc.) where the body of knowledge (knowledgebase) can beorganized in a variety of configurations, e.g., a structured repositoryof domain-specific information, such as ontologies, or unstructured datarelated to the domain, or a collection of natural language documentsabout the domain.

Content users input questions to the cognitive system which implementsthe QA pipeline. The QA pipeline then answers the input questions usingthe content in the corpus or data by evaluating documents, sections ofdocuments, portions of data in the corpus, or the like. When a processevaluates a given section of a document for semantic content, theprocess can use a variety of conventions to query such document from theQA pipeline, e.g., sending the query to the QA pipeline as a well-formedquestion which is then interpreted by the QA pipeline and a response isprovided containing one or more answers to the question. Semanticcontent is content based on the relation between signifiers, such aswords, phrases, signs, and symbols, and what they stand for, theirdenotation, or connotation. In other words, semantic content is contentthat interprets an expression, such as by using natural languageprocessing.

As will be described in greater detail hereafter, the QA pipelinereceives an input question, parses the question to extract the majorfeatures of the question, uses the extracted features to formulatequeries, and then applies those queries to the corpus of data. Based onthe application of the queries to the corpus of data, the QA pipelinegenerates a set of hypotheses, or candidate answers to the inputquestion, by looking across the corpus of data for portions of thecorpus of data that have some potential for containing a valuableresponse to the input question. The QA pipeline then performs deepanalysis on the language of the input question and the language used ineach of the portions of the corpus of data found during the applicationof the queries using a variety of reasoning algorithms. There may behundreds or even thousands of reasoning algorithms applied, each ofwhich performs different analysis, e.g., comparisons, natural languageanalysis, lexical analysis, or the like, and generates a score. Forexample, some reasoning algorithms may look at the matching of terms andsynonyms within the language of the input question and the foundportions of the corpus of data. Other reasoning algorithms may look attemporal or spatial features in the language, while others may evaluatethe source of the portion of the corpus of data and evaluate itsveracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the QA pipeline. The statisticalmodel is used to summarize a level of confidence that the QA pipelinehas regarding the evidence that the potential response, i.e., candidateanswer, is inferred by the question. This process is repeated for eachof the candidate answers until the QA pipeline identifies candidateanswers that surface as being significantly stronger than others andthus generates a final answer, or ranked set of answers, for the inputquestion.

As mentioned above, QA pipeline and mechanisms operate by accessinginformation from a corpus of data or information (also referred to as acorpus of content), analyzing it, and then generating answer resultsbased on the analysis of this data. Accessing information from a corpusof data typically includes: a database query that answers questionsabout what is in a collection of structured records, and a search thatdelivers a collection of document links in response to a query against acollection of unstructured data (text, markup language, etc.).Conventional question answering systems are capable of generatinganswers based on the corpus of data and the input question, verifyinganswers to a collection of questions for the corpus of data, correctingerrors in digital text using a corpus of data, and selecting answers toquestions from a pool of potential answers, i.e., candidate answers.

Content creators, such as article authors, electronic document creators,web page authors, document database creators, and the like, determineuse cases for products, solutions, and services described in suchcontent before writing their content. Consequently, the content creatorsknow what questions the content is intended to answer in a particulartopic addressed by the content. Categorizing the questions, such as interms of roles, type of information, tasks, or the like, associated withthe question, in each document of a corpus of data allows the QApipeline to more quickly and efficiently identify documents containingcontent related to a specific query. The content may also answer otherquestions that the content creator did not contemplate that may beuseful to content users. The questions and answers may be verified bythe content creator to be contained in the content for a given document.These capabilities contribute to improved accuracy, system performance,machine learning, and confidence of the QA pipeline. Content creators,automated tools, or the like, annotate or otherwise generate metadatafor providing information useable by the QA pipeline to identifyquestion and answer attributes of the content.

Operating on such content, the QA pipeline generates answers for inputquestions using a plurality of intensive analysis mechanisms whichevaluate the content to identify the most probable answers, i.e.,candidate answers, for the input question. The most probable answers areoutput as a ranked listing of candidate answers ranked according totheir relative scores or confidence measures calculated duringevaluation of the candidate answers, as a single final answer having ahighest ranking score or confidence measure, or which is a best match tothe input question, or a combination of ranked listing and final answer.

In an embodiment, a cognitive system with a QA pipeline can beunobtrusively integrated into a functioning call center in order for thecall center to realize the benefits of the advanced cognitive service.The integration can create a closed-loop system that can provide meansfor accelerated adaptation of the cognitive system in both aconfiguration and adaptation phase, as well as a run-time and feedbackphase.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of acognitive system 100 implementing a question and answer (QA) pipeline108 in a computer network 102. One example of a question/answergeneration operation which may be used in conjunction with theprinciples described herein is described in U.S. Patent ApplicationPublication No. 2011/0125734, which is herein incorporated by referencein its entirety. The cognitive system 100 is implemented on one or morecomputing devices 104 (comprising one or more processors and one or morememories, and potentially any other computing device elements generallyknown in the art including buses, storage devices, communicationinterfaces, and the like) connected to the computer network 102. Thenetwork 102 includes multiple computing devices 104 in communicationwith each other and with other devices or components via one or morewired and/or wireless data communication links, where each communicationlink comprises one or more of wires, routers, switches, transmitters,receivers, or the like. The cognitive system 100 and network 102 enablesquestion/answer (QA) generation functionality for one or more cognitivesystem users via their respective computing devices. Other embodimentsof the cognitive system 100 may be used with components, systems,sub-systems, and/or devices other than those that are depicted herein.

The cognitive system 100 is configured to implement a QA pipeline 108that receive inputs from various sources. For example, the cognitivesystem 100 receives input from the network 102, a corpus of electronicdocuments 140, cognitive system users, and/or other data and otherpossible sources of input. In one embodiment, some or all of the inputsto the cognitive system 100 are routed through the network 102. Thevarious computing devices 104 on the network 102 include access pointsfor content creators and QA system users. Some of the computing devices104 include devices for a database storing the corpus of data 140.Portions of the corpus of data 140 may also be provided on one or moreother network attached storage devices, in one or more databases, orother computing devices not explicitly shown in FIG. 1. The network 102includes local network connections and remote connections in variousembodiments, such that the cognitive system 100 may operate inenvironments of any size, including local and global, e.g., theInternet.

In one embodiment, the content creator creates content in a document ofthe corpus of data 140 for use as part of a corpus of data with thecognitive system 100. The document includes any file, text, article, orsource of data for use in the cognitive system 100. Call center QAsystem users access the cognitive system 100 via a network connection oran Internet connection to the network 102, and input questions to thecognitive system 100 that are answered by the content in the corpus ofdata 140. In one embodiment, the questions are formed using naturallanguage. The cognitive system 100 parses and interprets the questionvia a QA pipeline 108, and provides a response to the cognitive systemuser containing one or more answers to the question. In someembodiments, the cognitive system 100 provides a response to users in aranked list of candidate answers while in other illustrativeembodiments, the cognitive system 100 provides a single final answer ora combination of a final answer and ranked listing of other candidateanswers.

The cognitive system 100 implements the QA pipeline 108 which comprisesa plurality of stages for processing an input question and the corpus ofdata 140. The QA pipeline 108 generates answers for the input questionbased on the processing of the input question and the corpus of data140. The QA pipeline 108 will be described in greater detail hereafterwith regard to FIG. 3.

In some illustrative embodiments, the cognitive system 100 may be theIBM Watson™ cognitive system available from International BusinessMachines Corporation of Armonk, N.Y., which is augmented with themechanisms of the illustrative embodiments described hereafter. Asoutlined previously, a QA pipeline of the IBM Watson™ cognitive systemreceives an input question, which it then parses to extract the majorfeatures of the question, and which in turn are then used to formulatequeries that are applied to the corpus of data. Based on the applicationof the queries to the corpus of data, a set of hypotheses, or candidateanswers to the input question, are generated by looking across thecorpus of data for portions of the corpus of data that have somepotential for containing a valuable response to the input question. TheQA pipeline of the IBM Watson™ cognitive system then performs deepanalysis on the language of the input question and the language used ineach of the portions of the corpus of data found during the applicationof the queries using a variety of reasoning algorithms. The scoresobtained from the various reasoning algorithms are then weighted againsta statistical model that summarizes a level of confidence that the QApipeline of the IBM Watson™ cognitive system has regarding the evidencethat the potential response, i.e., candidate answer, is inferred by thequestion. This process is repeated for each of the candidate answers togenerate ranked listing of candidate answers which may then be presentedto the user that submitted the input question, or from which a finalanswer is selected and presented to the user. More information about theQA pipeline of the IBM Watson™ cognitive system may be obtained, forexample, from the IBM Corporation website, IBM Redbooks, and the like.For example, information about the QA pipeline of the IBM Watson™cognitive system can be found in Yuan et al., “Watson and Healthcare.”IBM developerWorks, 2011 and “The Era of Cognitive Systems: An InsideLook at IBM Watson and How it Works” by Rob High, IBM Redbooks, 2012.

As shown in FIG. 1, in accordance with some illustrative embodiments,the cognitive system 100 is further augmented, in accordance with themechanisms of the illustrative embodiments, to include logic implementedin specialized hardware, software executed on hardware, or anycombination of specialized hardware and software executed on hardware,for integrating a call center question and answer (QA) system 120.

The call center QA system can include an instant messaging (IM)application 121, which can have one or more chat channels that can beused by one or more call center agents and/or one or more subject matterexperts (SME). Traditionally, if a call center agent encounters aquestion they cannot answer, the call center agent will contact thesubject matter expert through the chat channel to obtain the correctanswer, which the call center agent can then relay to the customermaking the initial inquiry. Alternate embodiments contemplate othertechnical support or help desk models using agent and expertinteractions.

To interact with the instant messaging application 121, the call centerQA system 120 can have a plug-in module 123 that can interface with theIM application 121 in real-time in order to begin to collect the datatransmitted through the IM application 121. The IM application 121 usedcan provide a way for the plug-in module 123 to register itself with theIM application 121 and one or more protocols for exchanging data. Thiscan be accomplished through the use of one or more registration commands125, which can be used to send questions from the IM application 121 tothe cognitive system 100.

A registration command 125 can be prepended to every user entry, and cansend the text (or utterance) entered into the IM application 121 to theplug-in module 123, which can be designed to call the cognitive system100 through a request method (for example, HTTP POST). Entered textrouted to the cognitive system 100 can be designated by token, team ID,channel ID, channel name, user ID, and/or user name. At the cognitivesystem 100 level, the information can be received from an outboundcommand or webhook. At runtime, the utterance can be processed and, if aconfident response is produced by the cognitive system 100, the responsecan be returned to the IM application 121 through an incoming command orwebhook. In an embodiment, the threshold for response confidence can becontrolled by the cognitive system 100.

One or more utterances received from call center agent/SME interactionscan be subjected to natural language processing techniques of thecognitive system 100 and/or call center QA system 120 to transform thequestions into acyclic graphs where nodes represent potential facts, andconnectors represent the overall connections between the potentialfacts. This operation may be performed, for example, as part of aningestion operation of the cognitive system 100 which reads the naturallanguage text of the electronic documents or asked questions, parses thenatural language text and performs natural language processing on thenatural language text, including performing annotation operations usingannotators, to extract key features and facts of the natural languagetext which are then converted to the acyclic graphs.

The acyclic graphs of the analyzed QA pairs are stored in storage device150 associated with either the cognitive system 100 or the call centerQA system 120, where the storage device 150 may be a memory, a hard diskbased storage device, flash memory, solid state storage device, or thelike (hereafter assumed to be a “memory” with in-memory representationsof the acyclic graphs for purposes of description). The in-memoryacyclic graphs are then analyzed by the reusable branch engine 122 ofthe call center QA system 120 to identify reusable branches within theacyclic graphs and a reusable branch data structure having entries foreach reusable branch found in this way, and other reusable brancheseither found in other corpora, readily known and pre-populated in thereusable branch data structure by subject matter experts, or the like,is generated. The identification of the reusable branches may further beassociated with the in-memory acyclic graph of the correspondingquestion as well so as to identify for the particular knowledge domainwhat the reusable branches are in the knowledge domain.

Either as part of an ingestion operation, or by the QA acyclic graphanalysis engine 124 analyzing the acyclic graphs generated by theingestion operation, a QA element data structure 126 defining thevarious knowledge domains in the ingested corpus 140, as well as otherQA elements pre-populated in the QA element data structure 126 eitherthrough analysis of other corpora or through manual input by subjectmatter expert/call center agent interaction, is generated.

The QA element data structure 126 is analyzed by clustering engine 128to identify clusters of QA elements based on their characteristics. Forexample, QA elements may be clustered according to similar QA elementtypes to form QA element clusters. These clusters may be stored in acluster data structure 129. As noted above, some QA elements may havesub-elements and various levels of clustering may be performed, whichmay be classified/clustered into other clusters. Thus, the same QAelement may be present in multiple clusters.

In response to receiving the input utterance, a similar QA elementsearch engine 130 performs a search of the cluster data structure 129 togenerate a listing of the QA element clusters that involve the given newQA element(s). In making this list, the cluster search engine 130 mayanalyze the clusters of reusable branches that contain the new QAelement to produce an initial list of candidate QA elements. Thislisting is then extended with candidate QA elements for similar QAelements obtained from clusters with which the elements of the reusablebranches involving the new QA element are clustered.

Alternatively, the clustering performed by the clustering engine 128 maybe performed after the identification of similar QA elements to those ofthe reusable branches found as having the new QA element(s), performedby the similar QA element search engine 130 and the list may then beextended with candidate QA elements for similar QA elements by using aprovided QA ontology data structure 132. Those candidate elements may beincluded in the listing and the listing may be analyzed by theclustering engine 128 to generate clusters of QA elements for storage inthe cluster data structure 129.

In either case, the similar QA element search engine 130 then determineswhether the presented question already contains any of the clusters ofcandidate QA elements, i.e., the clusters identified as having the newQA element(s) in the request. For those that are already present withinthe utterance, the candidate clusters may be promoted in the listing togenerate a filtered listing of candidate QA elements and their clusters134.

The QA element clusters in the filtered listing of candidate QA elements134 are then analyzed by a QA element compatibility engine 136 toidentify which of the element clusters are compatible with the knowledgedomain of the question that is to be answered. The QA elementcompatibility engine 136 may utilize configured association ruleslearned during a training of the call center QA system 120 and knowledgebase, where the association rules specify compatibility of QA elementswith different knowledge domains. Using these association rules, the QAelement compatibility engine determines what combinations or patterns ofone or more QA elements are found in questions asked by subject matterexperts working in the same knowledge domain. The intersection of theassociation rules with the candidate QA element clusters indicates whichelement clusters are compatible with the knowledge domain. The resultingcandidate clusters that intersect with the association rules may then beranked by the QA element compatibility engine 136, such as based onfrequency of appearance of the clusters or QA elements in the clusters.Other ranking criteria may also be utilized as noted above.

A QA element cluster in the filtered listing of candidate clusters 134,which also intersects with one or more of the association rules, isselected by the QA element compatibility engine 136 for use in providingan answer to a question. This selection may be based on the ranking ofthe clusters intersecting the association rules as discussed above. Forexample, a top ranked cluster may be selected for use in presenting ananswer to the utterance. Alternatively, other selection criteria may beutilized as well, such as in an implementation where ranking of theclusters may not be performed, as previously discussed above.

Additionally, subject matter experts and call center manager may makeuse of a feedback module 127, which can interact with the cognitivesystem 100 in essentially the same manner as the IM application 121, inorder to evaluate and analyze answers provided by the cognitive system100 in response to one or more utterances, allowing for further trainingof the cognitive system 100. In an embodiment, the feedback module 127can be incorporated into the IM application 121 to provide seamlessfeedback. Thus, the mechanisms of the illustrative embodiments providean intelligent cognitive system 100 for integration into a call centerquestion and answer system 120 that can allow one or more call centeragents to interact with a cognitive system 100 as if it were a subjectmatter expert.

FIG. 2 is a block diagram of an example data processing system 200 inwhich aspects of the illustrative embodiments are implemented. Dataprocessing system 200 is an example of a computer, such as a server orclient, in which computer usable code or instructions implementing theprocess for illustrative embodiments of the present invention arelocated. In one embodiment, FIG. 2 represents a server computing device,such as a server, which implements the call center QA system 120 andcognitive system 100 described herein.

In the depicted example, data processing system 200 can employ a hubarchitecture including a north bridge and memory controller hub (NB/MCH)201 and south bridge and input/output (I/O) controller hub (SB/ICH) 202.Processing unit 203, main memory 204, and graphics processor 205 can beconnected to the NB/MCH 201. Graphics processor 205 can be connected tothe NB/MCH through an accelerated graphics port (AGP).

In the depicted example, the network adapter 206 connects to the SB/ICH202. The audio adapter 207, keyboard and mouse adapter 208, modem 209,read only memory (ROM) 210, hard disk drive (HDD) 211, optical drive (CDor DVD) 212, universal serial bus (USB) ports and other communicationports 213, and the PCl/PCIe devices 214 can connect to the SB/ICH 202through bus system 216. PCl/PCIe devices 214 may include Ethernetadapters, add-in cards, and PC cards for notebook computers. ROM 210 maybe, for example, a flash basic input/output system (BIOS). The HDD 211and optical drive 212 can use an integrated drive electronics (IDE) orserial advanced technology attachment (SATA) interface. The super I/O(SIO) device 215 can be connected to the SB/ICH.

An operating system can run on processing unit 203. The operating systemcan coordinate and provide control of various components within the dataprocessing system 200. As a client, the operating system can be acommercially available operating system. An object-oriented programmingsystem, such as the Java™ programming system, may run in conjunctionwith the operating system and provide calls to the operating system fromthe object-oriented programs or applications executing on the dataprocessing system 200. As a server, the data processing system 200 canbe an IBM® eServer™ System p® running the Advanced Interactive Executiveoperating system or the Linux operating system. The data processingsystem 200 can be a symmetric multiprocessor (SMP) system that caninclude a plurality of processors in the processing unit 203.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as the HDD 211, and are loaded into the main memory 204 forexecution by the processing unit 203. The processes for embodiments ofthe call center QA system can be performed by the processing unit 203using computer usable program code, which can be located in a memorysuch as, for example, main memory 204, ROM 210, or in one or moreperipheral devices.

A bus system 216 can be comprised of one or more busses. The bus system216 can be implemented using any type of communication fabric orarchitecture that can provide for a transfer of data between differentcomponents or devices attached to the fabric or architecture. Acommunication unit such as the modem 209 or network adapter 206 caninclude one or more devices that can be used to transmit and receivedata.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIG. 2 may vary depending on the implementation. Otherinternal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives may be used inaddition to or in place of the hardware depicted. Moreover, the dataprocessing system 200 can take the form of any of a number of differentdata processing systems, including but not limited to, client computingdevices, server computing devices, tablet computers, laptop computers,telephone or other communication devices, personal digital assistants,and the like. Essentially, data processing system 200 can be any knownor later developed data processing system without architecturallimitation.

FIG. 3 illustrates a QA system pipeline, of a cognitive system, forprocessing an input utterance in accordance with one illustrativeembodiment. The QA system pipeline of FIG. 3 may be implemented, forexample, as QA pipeline 108 of cognitive system 100 in FIG. 1. It shouldbe appreciated that the stages of the QA pipeline shown in FIG. 3 areimplemented as one or more software engines, components, or the like,which are configured with logic for implementing the functionalityattributed to the particular stage. Each stage is implemented using oneor more of such software engines, components or the like. The softwareengines, components, etc., are executed on one or more processors of oneor more data processing systems or devices and utilize or operate ondata stored in one or more data storage devices, memories, or the like,on one or more of the data processing systems. The QA pipeline of FIG. 3is augmented, for example, in one or more of the stages to implement theimproved mechanism of the illustrative embodiments described hereafter,additional stages may be provided to implement the improved mechanism,or separate logic from the pipeline 108 may be provided for interfacingwith the pipeline 108 and implementing the improved functionality andoperations of the illustrative embodiments.

As shown in FIG. 3, the QA pipeline 108 comprises a plurality of stages310-380 through which the cognitive system operates to analyze an inputquestion and generate a final response. In an initial question inputstage 310, the QA pipeline 108 receives an input question that ispresented in a natural language format. That is, a user inputs, via auser interface, an input question for which the user wishes to obtain ananswer, e.g., “Who are Washington's closest advisors?” In response toreceiving the input question, the next stage of the QA pipeline 108,i.e., the question and topic analysis stage 320, parses the inputquestion using natural language processing (NLP) techniques to extractmajor features from the input question, and classify the major featuresaccording to types, e.g., names, dates, or any of a plethora of otherdefined topics. For example, in the example question above, the term“who” may be associated with a topic for “persons” indicating that theidentity of a person is being sought, “Washington” may be identified asa proper name of a person with which the question is associated,“closest” may be identified as a word indicative of proximity orrelationship, and “advisors” may be indicative of a noun or otherlanguage topic.

In addition, the extracted major features include key words and phrasesclassified into question characteristics, such as the focus of thequestion, the lexical answer type (LAT) of the question, and the like.As referenced to herein, a lexical answer type (LAT) is a word in, or aword inferred from, the input question that indicates the type of theanswer, independent of assigning semantics to that word. For example, inthe question “What maneuver was invented in the 1500s to speed up thegame and involves two pieces of the same color?” the LAT is the string“maneuver.” The focus of a question is the part of the question that, ifreplaced by the answer, makes the question a standalone statement. Forexample, in the question “What drug has been shown to relieve thesymptoms of ADD with relatively few side effects?,” the focus is “drug”since if this word were replaced with the answer, e.g., “Adderall,” theanswer can be used to replace the term “drug” to generate the sentence“Adderall has been shown to relieve the symptoms of ADD with relativelyfew side effects.” The focus often, but not always, contains the LAT. Onthe other hand, in many cases it is not possible to infer a meaningfulLAT from the focus.

Referring again to FIG. 3, the identified major features are then usedduring the question decomposition stage 330 to decompose the questioninto one or more queries that are applied to the corpora ofdata/information 345 in order to generate one or more hypotheses. Thequeries are generated in any known or later developed query language,such as the Structure Query Language (SQL), or the like. The queries areapplied to one or more databases storing information about theelectronic texts, documents, articles, websites, and the like, that makeup the corpora of data/information 345. That is, these various sourcesthemselves, different collections of sources, and the like, represent adifferent corpus 347 within the corpora 345. There may be differentcorpora 347 defined for different collections of documents based onvarious criteria depending upon the particular implementation. Forexample, different corpora may be established for different topics,subject matter categories, sources of information, or the like. As oneexample, a first corpus may be associated with healthcare documentswhile a second corpus may be associated with financial documents.Alternatively, one corpus may be documents published by the U.S.Department of Energy while another corpus may be IBM Redbooks documents.Any collection of content having some similar attribute may beconsidered to be a corpus 347 within the corpora 345.

The queries are applied to one or more databases storing informationabout the electronic texts, documents, articles, websites, and the like,that make up the corpus of data/information, e.g., the corpus of data140 in FIG. 1. The queries are applied to the corpus of data/informationat the hypothesis generation stage 340 to generate results identifyingpotential hypotheses for answering the input question, which can then beevaluated. That is, the application of the queries results in theextraction of portions of the corpus of data/information matching thecriteria of the particular query. These portions of the corpus are thenanalyzed and used, during the hypothesis generation stage 340, togenerate hypotheses for answering the input question. These hypothesesare also referred to herein as “candidate answers” for the inputquestion. For any input question, at this stage 340, there may behundreds of hypotheses or candidate answers generated that may need tobe evaluated.

The QA pipeline 108, in stage 350, then performs a deep analysis andcomparison of the language of the input question and the language ofeach hypothesis or “candidate answer,” as well as performs evidencescoring to evaluate the likelihood that the particular hypothesis is acorrect answer for the input question. As described in FIG. 1, thisinvolves using a plurality of reasoning algorithms, each performing aseparate type of analysis of the language of the input question and/orcontent of the corpus that provides evidence in support of, or not insupport of, the hypothesis. Each reasoning algorithm generates a scorebased on the analysis it performs which indicates a measure of relevanceof the individual portions of the corpus of data/information extractedby application of the queries as well as a measure of the correctness ofthe corresponding hypothesis, i.e., a measure of confidence in thehypothesis. There are various ways of generating such scores dependingupon the particular analysis being performed. In general, however, thesealgorithms look for particular terms, phrases, or patterns of text thatare indicative of terms, phrases, or patterns of interest and determinea degree of matching with higher degrees of matching being givenrelatively higher scores than lower degrees of matching.

In the synthesis stage 360, the large number of scores generated by thevarious reasoning algorithms are synthesized into confidence scores orconfidence measures for the various hypotheses. This process involvesapplying weights to the various scores, where the weights have beendetermined through training of the statistical model employed by the QApipeline 108 and/or dynamically updated. For example, the weights forscores generated by algorithms that identify exactly matching terms andsynonyms may be set relatively higher than other algorithms that areevaluating publication dates for evidence passages. The weightsthemselves may be specified by subject matter experts or learned throughmachine learning processes that evaluate the significance ofcharacteristics evidence passages and their relative importance tooverall candidate answer generation.

The weighted scores are processed in accordance with a statistical modelgenerated through training of the QA pipeline 108 that identifies amanner by which these scores may be combined to generate a confidencescore or measure for the individual hypotheses or candidate answers.This confidence score or measure summarizes the level of confidence thatthe QA pipeline 108 has about the evidence that the candidate answer isinferred by the input question, i.e., that the candidate answer is thecorrect answer for the input question.

The resulting confidence scores or measures are processed by a finalconfidence merging and ranking stage 370 which compares the confidencescores and measures to each other, compares them against predeterminedthresholds, or performs any other analysis on the confidence scores todetermine which hypotheses/candidate answers are the most likely to bethe correct answer to the input question. The hypotheses/candidateanswers are ranked according to these comparisons to generate a rankedlisting of hypotheses/candidate answers (hereafter simply referred to as“candidate answers”). From the ranked listing of candidate answers, atstage 380, a final answer and confidence score, or final set ofcandidate answers and confidence scores, are generated and output to thesubmitter of the original input question via a graphical user interfaceor other mechanism for outputting information.

FIG. 4 illustrates a flowchart showing the integration process by whicha cognitive system can be introduced into a working call center withoutdisrupting the call center's functionality, according to illustrativeembodiments as described herein. In the configuration phase 410, the IMapplication can be connected to the cognitive system 401 as previouslydescribed in FIG. 1. As part of the connection, the untrained cognitivesystem can be connected to a particular call center agent chat channel402.

Once integrations have been established between the IM application andthe cognitive system, the call center QA system can enter one of twomodes: listen-only mode 411 and answer mode 412. In listen-only mode411, the cognitive system can receive and collect all utterances inreal-time from the chat channel 402 into a corpus. Utterances caninclude both call center agent questions as well as the answers providedby the subject matter experts or call center managers. In an embodiment,all utterances that do not include a question or answer can be excludedfrom collection. After collection, the cognitive system can be trainedusing the corpus of question and answer pairs 404 generated from theutterances 403, which can be generated in the manner described in FIGS.1 and 3. In listen-only mode, the system can internally propose answersto one or more additional questions asked by the call center agents,analyze those answers, and incorporate the analysis of the proposedanswers into future question analysis. All ingestion, analysis, andanswer generation can be performed without any external notificationsmade to the call center agents, call center managers, or subject matterexperts. On-topic questions can be used to establish ground truth andtrain the cognitive system in accordance to best practices for adaptinga probabilistic cognitive system for the particular use case.Additionally, one or more experiments can be run in listen-only mode 411to ensure the call center QA system is running at an accuracy levelacceptable to move into answer mode 412.

In answer mode 412, the cognitive system can receive all utterances fromone or more chat channels. However, in answer mode 412, the cognitivesystem can join the chat channel as a member of the team 405, withpermissions to provide responses to utterances posed by call centeragents. Each utterance can be processed by the cognitive system, and ifa confident response if produced, the response can be passed back outthe chat channel as an answer. Confidence thresholds can be controlledby the cognitive system. In an embodiment, all members of the chatchannel can have the ability to see the response generated by thecognitive system. The cognitive system answers can be reviewed andcorrected by human members 406. In an embodiment, expert team membersand call center managers can decide if the answer is appropriate, andcan flag incorrect or incomplete answers 407. In an alternateembodiment, flagged answers will not show to all members of the chatchannel. Over time, the cognitive system can improve based on analysisof the flagged answers, leading to a higher number of answers exceedingthe confidence threshold, which in turn leads to more questions beinganswered by the cognitive system without human aid 408. As the cognitivesystem assumes a higher workload, the call center managers and subjectmatter experts can focus on particularly difficult questions, whichallows the knowledge to scale.

The system and processes of the figures are not exclusive. Othersystems, processes and menus may be derived in accordance with theprinciples of embodiments described herein to accomplish the sameobjectives. It is to be understood that the embodiments and variationsshown and described herein are for illustration purposes only.Modifications to the current design may be implemented by those skilledin the art, without departing from the scope of the embodiments. Asdescribed herein, the various systems, subsystems, agents, managers andprocesses can be implemented using hardware components, softwarecomponents, and/or combinations thereof. No claim element herein is tobe construed under the provisions of 35 U.S.C. 112, sixth paragraph,unless the element is expressly recited using the phrase “means for.”

Although the invention has been described with reference to exemplaryembodiments, it is not limited thereto. Those skilled in the art willappreciate that numerous changes and modifications may be made to thepreferred embodiments of the invention and that such changes andmodifications may be made without departing from the true spirit of theinvention. It is therefore intended that the appended claims beconstrued to cover all such equivalent variations as fall within thetrue spirit and scope of the invention.

What is claimed is:
 1. A method of training a cognitive system in aworking call center, the method comprising: receiving one or morequestions collected through an instant messenger application; receivingone or more answers collected through the instant messenger application,wherein the one or more answers are associated with the one or morequestions; receiving an additional question collected through theinstant messenger application; determining a proposed answer to theadditional question comprising: transforming the additional questioninto an acyclic graph to determine a knowledge domain of the additionalquestion; identifying element clusters associated with the one or morequestions and the one or more answers that are compatible with theknowledge domain of the additional question; ranking the identifiedelement clusters; and selecting the identified element clusters rankedhighest for use in determining the proposed answer; and analyzing theproposed answer to train the cognitive system.
 2. The method of claim 1,further comprising: incorporating the analysis of the proposed answerinto an analysis of future questions.
 3. The method of claim 2, furthercomprising: wherein the analysis of the proposed answer determines alikelihood that the proposed answer is a correct answer to theadditional question by performing a deep analysis using a plurality ofreasoning algorithms.
 4. The method of claim 1, further comprising:wherein the analysis of the proposed answer comprises receiving anevaluation of the proposed answer from a subject matter expert; andincorporating the evaluation of the proposed answer into an analysis offuture questions.
 5. The method of claim 1, further comprising: definingan element data structure comprising one or more knowledge domains ofthe questions and the answers; and analyzing the element data structureto identify the element clusters based on their characteristics.
 6. Themethod of claim 1, further comprising: outputting the proposed answerthrough the instant messenger application when the cognitive system haspermission to provide the proposed answer.
 7. The method of claim 6,further comprising: storing similar element clusters in one or morecluster data structures.
 8. The method of claim 7, further comprising:searching the one or more cluster data structures to identify theelement clusters associated with the one or more questions and the oneor more answers that are compatible with the knowledge domain of theadditional question.
 9. The method of claim 1, further comprising:wherein the acyclic graphs comprise nodes representing potential factsand connectors representing the overall connections between thepotential facts.
 10. The method of claim 3, further comprising: whereineach reasoning algorithm performs a separate type of analysis of thelanguage of the additional question language and the language of theproposed answer.
 11. A call center question and answer systemcomprising: an instant messaging application, wherein the instantmessaging application has one or more chat channels; a plug-in module,wherein the plug-in module is configured to interface with the instantmessaging application; and a cognitive system integrated into the callcenter question and answer system, wherein the cognitive system isconfigured to receive data from the instant messaging application andperform a deep analysis and comparison of one or more input questionsand one or more candidate answers.
 12. The system as recited in claim11, further comprising: wherein the plug-in module is configured tointerface with the instant messaging application in real-time.
 13. Thesystem as recited in claim 11, further comprising: wherein the plug-inmodule is configured to collect data transmitted through the instantmessaging application.
 14. The system as recited in claim 11, furthercomprising: wherein the plug-in module is configured to register itselfwith the instant messaging application using the one or moreregistration commands.
 15. The system as recited in claim 11, furthercomprising: wherein the plug-in module is configured to collect datausing one or more registration commands.
 16. The system as recited inclaim 11, further comprising: wherein the cognitive system is configuredto do deep analysis using a plurality of reasoning algorithms, eachperforming a separate type of analysis of the language of the one ormore input questions and the one or more candidate answers.
 17. Thesystem as recited in claim 16, further comprising: wherein eachreasoning algorithm generates a score based on the analysis performed bythe reasoning algorithm, wherein the score is a measure of correctnessfor the one or more candidate answers.
 18. The system as recited inclaim 17, further comprising: wherein the cognitive system is configuredto synthesize the score from each reasoning algorithm into a confidencescore for each candidate answer.
 19. The system as recited in claim 18,further comprising: wherein the cognitive system is configured to rankthe confidence scores and compare them to predetermined thresholds. 20.A computer implemented method, in a data processing system comprising aprocessor and a memory comprising instructions which are executed by theprocessor to cause the processor to implement training for a cognitivesystem in a working call center, the method comprising: receiving one ormore answers collected through the instant messenger application,wherein the one or more answers are associated with the one or morequestions; receiving an additional question collected through theinstant messenger application; providing a proposed answer to theadditional question comprising: transforming the additional questioninto an acyclic graph to determine a knowledge domain of the additionalquestion; identifying element clusters associated with the one or morequestions and the one or more answers that are compatible with theknowledge domain of the additional question; ranking the identifiedelement clusters; selecting the identified element clusters rankedhighest for use in determining the proposed answer; and analyzing theproposed answer to train the cognitive system. analyzing the proposedanswer, wherein the analysis of the proposed answer determines alikelihood that the proposed answer is a correct answer to theadditional question by performing a deep analysis using a plurality ofreasoning algorithms; and incorporating the analysis of the proposedanswer into an analysis of future questions.